Technological Thoughts by Jerome Kehrli

Big Data and private banking, what for ?

by Jerome Kehrli

Posted on Wednesday Oct 05, 2016 at 10:50AM in Computer Science

Big Data technologies are increasingly used in retail banking institutions for customer profiling or other marketing activities. In private banking institutions, however, applications are less obvious and there are only very few initiatives.
Yet, as a matter of fact, there are opportunities in such institutions and they can be quite surprising.

Big Data technologies, initiated by the Web Giants such as Google or Amazon, enable to analyze very massive amount of data (ranging from Terabytes to Petabytes). Apache Hadoop is the de-facto standard nowadays when it comes to considering Open Source Big Data technologies but it is increasingly challenged by alternatives such as Apache Spark or others providing less constraining programming paradigms than Map-Reduce.

These Big Data Processing Platform benefits from the NoSQL genes : the CAP Theorem when it comes to storing data, the usage of commodity hardware, the capacity to scale-out (almost) linearly (instead of scaling up your Oracle DB) and a much lower TCO (Total Cost of Ownership) than standard architectures.

Most essential applications for such technologies in retail banking institutions consist in gathering knowledge and insights on the customer base, customer's profiles and their tendencies by using cutting-edge Machine Learning techniques on such data.

In contrary to retail banking institutions that are exploiting such technologies for many years, private banking institution, with their very low amount of transactions and their limited customer base are considering these technologies with a lot of skepticism and condescension.

However, in contrary to preconceived ideas, use case exist and present surprising opportunities, mostly around three topics :

  • Enhance proximity with customers
  • Improve investment advisory services
  • Reduce computation costs

Enhance proximity with customers

Private banking institutions are increasingly challenged by new external asset management business models such as multi-family offices. These new kind of financial services firms provide their customers with a very high level of personalized service and a very close relationship management, up to a certain form of intimacy, able to seduce UHNWI (Ultra-High Net Worth Individuals).

The progressive denormalization of customer data, their transactions and all the other kinds of related data, even very indirectly, inside a hadoop cluster, then their massive exploitation with cutting-edge machine learning techniques enables traditional institutions to sharpen and refine their knowledge of their customer.
This consists in importing within hadoop all the different data in an incremental way. After every new stage, one needs to study carefully the new analysis opportunities.

The new knowledge and insights gained this way enables private banking institutions to reach a level of proximity, understanding of their customers and customized investment advisory services close to family offices. They may this way easier keep their top customers seduced by such asset management models.

Improve investment advisory services

Customer profiling to find out about tendencies in terms in investment in peer groups propose a certain interest. Profiles can be examined from various perspective and angles by combining characteristics of customers such as their age, origins, wealth level, activity sector or even their family situation.

The banking institutions should typically use the same hadoop cluster deployed for the above use case that already holds all the required information for such analysis.

There are several objectives there. For instance one might want to adapt investment advices by comparing a specific customer situation with the profile of her peer group or simply with the general tendencies of the market.
Another example is related to the advanced possibilities offered by such knowledge when it comes to optimize investigations on investments or unusual customers most essentially to detect frauds.

Reduce computation costs

For instance, recent NoSQL technologies such as Cassandra are very efficient when it comes to storing and manipulating time series. Infinispan enables to structure an impressive amount of information in memory for massive and complex computations. One could also mention Storm, very efficient when it comes to analyzing market events in real-time.
These new technologies from the Big Data / NoSQL landscape offer unprecedented opportunities for quantitative research on very massive amount of market data.

They have the opportunity to form a little revolution in the world of private banking institutions who finally have very cheap ways for massive and real-time computations of key risk and performance metrics. Even better, these same platforms can be used for optimization, rebalancing or even simulation of financial portfolios at a very large scale and in near-real time. They provide interesting alternatives to more traditional approaches such as Bloomberg's analytical platform or other very expensive home-made developments on terradata.

Deploying such ambitious technologies inside a private banking institutions is however not innocuous. Adopting an iterative approach consisting in building the software bricks, importing the data and implementing analysis step by step is a key factor of success.

Typical Architecture

A typical architecture of such system would be as follows :

big data in private banking architecture
(Image is copyrighted OCTO Technology SA / 2014)

(Note : Sorry for bragging but I'm quite proud of this schema. I designed it in late 2013 when I was consultant for OCTO Technology. If you look carefully at it, it is pretty close to a first proposal of Lambda Architecture for financial institutions with hadoop being the batch layer and Infinispan the speed layer, and that way before Lambda Architecture was discussed so widely.)

(Paper originally published by myself in ICT Journal / March 2014)

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